Forecasting Content Popularity: a Recommendation Method for Aggregating Predictors

Autor: Nguyen-Thanh, Nhan, Khawam, Kinda, Lohan, Elena Simona, Marinca, Dana, Martin, Steven, Quadri, Dominique, Boukhatem, Lila
Přispěvatelé: Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Parallélisme, Réseaux, Systèmes, Modélisation (PRISM), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Centre d'Enseignement et de Recherche en Informatique - CERI-Avignon Université (AU)
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Knowledge and Information Systems (KAIS)
Knowledge and Information Systems (KAIS), Springer, In press
ISSN: 0219-1377
0219-3116
Popis: International audience; Currently, Video on Demand (VoD) is the heaviest data-driven service among various multimedia services. Therefore, predicting the popularity of mul-timedia contents, particularly video contents, in order to supply their proactive caching is a crucial issue. Mobile Edge Computing is proposed as a general solution for the upcoming 5G-based networks to allow computation and storage capabilities at the edge of Radio Access Network. Henceforth, applications and services can be deployed near the end-user, improving the backhaul resources utilization, energy consumption, and users Quality of Experience thanks to the reduction of traffic latency. Therefore, the pivotal problem addressed in this paper is to find the most appropriate prediction methods from a large set of predefined methods, able to forecast, with high accuracy, the multimedia content popularity. A real YouTube dataset was used for simulations. The proposed Recommendation System framework is very flexible because it can be extended to different time-series prediction contexts. It also displays good scalability and can be dynamically adapted for analyzing huge sets of time-series with large sets of predictors.
Databáze: OpenAIRE
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